Estimating time to reinjury among Washington State injured workers by degree of permanent impairment: Using state wage data to adjust for time at risk
Bibliographic record
Abstract
BACKGROUND: Many injured workers are reinjured, but reinjury risk is challenging to quantify. Because many injured workers face delayed return-to-work, or return to part-time or intermittent jobs, a calendar timescale may overestimate actual work-time at risk, yielding underestimated reinjury rates. Objectives included determining: (1) reinjury risk by degree of permanent impairment and other factors, and (2) how choice of timescale affects reinjury estimates. METHODS: This retrospective cohort study included Washington State workers' compensation (WC) claims for 43,114 injured workers, linked to state wage files (2003-2018). Three timescales were used to define at-risk denominators: (1) calendar quarters; (2) quarters with any wages; and (3) full-time equivalent (FTE) quarters, defined as cumulative work hours ÷ 520. Associations between reinjury outcomes and worker, injury, job, and WC vocational rehabilitation program participation characteristics were assessed using Cox proportional hazards regression. RESULTS: Overall reinjury rates were 5.9 per 100 worker-years using a calendar timescale (95% confidence interval [CI]: 5.8-6.0), 10.0 using any-wage quarters (95% CI: 9.9-10.2), and 12.5 using FTE quarters (95% CI: 12.3-12.7). Reinjury rates were highest in the first two quarters after initial injury, remaining elevated for about 4 years. Using FTE quarters, workers with ≥10% whole body impairment had a 34% higher risk of reinjury relative to workers with no permanent partial disability award (95% CI: 1.25-1.44); no difference was detected using calendar time. CONCLUSIONS: Timescale substantially affects reinjury estimates and comparisons between groups with differential return-to-work patterns. Linking wage data to WC claims facilitates measurement of long-term employment, yielding more accurate reinjury estimates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".